Many de-interlacing methods have been published in literature and/or are being commercially used in various products. Methods range from simple spatial de-interlacers to motion-compensated de-interlacers.
The category of spatial de-interlacers, however, is important, as it is often used as a kind of fall-back in case the motion vectors used in the interlacing processes are unreliable, but moreover, it is the category of de-interlacers relied on in low-cost systems. Overviews of de-interlacers (including spatial ones) can be found in G. de Haan and E. B. Bellers, ‘De-interlacing—An overview’, The proceedings of the IEEE, vol. 86, no. 9, pp. 1839-1857, September 1998 and E. B. Bellers and G. de Haan, ‘De-interlacing—A key technology for Scan Rate Conversion’, Advances in Image Communications, Vol. 9, ISBN 0-444-50594-6, Elsevier Science B. V., 2000. Another spatial de-interlacer that is not included in the References above can be found in G. de Haan and R. Lodder, ‘De-interlacing of video data using motion vectors and edge information’, Digest of the ICCE'02, pp. 70-71, June 2002.
In the above references, edge information is utilized to improve the de-interlacing quality. This type of de-interlacer is referred to as an EDDI (Edge Dependent De-Interlacing) type of de-interlacer. Although EDDI exceeds the quality level of many other spatial de-interlacers, its quality level is insufficient for the video and digital video demands of the near future. Moreover, the complexity and memory requirements are relatively high for spatial de-interlacers. Furthermore, some general ideas for a new type of directional de-interlacer are presented in the preceding papers, but at present there is no means to calculate the reliability of a directional interpolation.
In addition and as discussed in the above papers, there has been an attempt to provide a “sort of mix between a spatial and a temporal de-interlacer. An example of such a mix is, the Adaptive Recursive de-interlacer (see, G. de Haan and E. B. Bellers, ‘De-interlacing—An overview’, The proceedings of the IEEE, vol. 86, no. 9, pp. 1839-1857, September 1998 and E. B. Bellers and G. de Haan, ‘De-interlacing—A key technology for Scan Rate Conversion’, Advances in Image Communications, Vol. 9, ISBN 0-444-50594-6, Elsevier Science B. V., 2000) which calculates how well vertical neighboring samples can be created from the previous de-interlaced picture. This match provides a metric for reliability of the temporal interpolation, and as such, is used to mix between the temporal and spatial interpolation. Another example that is found in the two above articles was proposed by Bock. Bock proposed to mix between a spatial and temporal de-interlacer based on the output of a motion detector, i.e. if motion is detected, there is a bias towards the spatial de-interlacer and otherwise bias towards the temporal de-interlacer.
These algorithms have a linear relationship between an error/reliability metric (e.g. how well can the current field be regenerated out of the previous de-interlaced picture, or how likely is the current pixel a part of a moving object, etc) and the mix factor used to mix the spatial and temporal de-interlacer. Moreover, the algorithms rely heavily on a single error criterion.